Current-generation High Performance Computing (HPC) based modeling environments for socio-technical systems are complex, large-scale special-purpose systems. These systems are readily accessible only to a limited number of highly specialized technical personnel in narrowly defined software applications. In other words, currently, web oriented distributed information CI for representing and analyzing large socio-technical systems simply do not exist. The vision of the CI under development is to change the model of delivery of HPC-oriented models and analytical tools to analysts. Just as the advent of search engines (e.g., Google) radically altered research and analysis of technical subjects across the board, the goal of the CI is to make HPC resources seamless, invisible, and indispensable in routine analytical efforts by demonstrating that HPC resources should be organized as an evolving commodity, and made accessible in a fashion as ubiquitous as Google’s home page. Recently, grid based global cyber-infrastructures for modeling physical systems have been deployed. This is only a first step in developing what we would call semantic complex system modeling; we hope to make similar progress in the context of socio-technical systems. Developing such an infrastructure poses unique challenges that are often different than the ones faced by researchers developing the CI for physical systems. We outline some of these below.
1. Scalability:The CI must be globally scalable. The scalability comes in three forms: (i) allowing multiple concurrent users, (ii) processing huge quantities of distributed data and (iii) ability to execute large national-scale models. For example, simulating dynamical processes usually occurs on unstructured, time varying networks. Unlike many physical science applications, the unstructured network is crucial in computing realistic estimates, e.g., disease dynamics in an urban region. The unstructured network represents the underlying coupled, complex social contact and infrastructure. We need to simulate large portions of the continental United States – this implies a time varying dynamic social network of over 250 million nodes.
2. Coordination: The CI should allow computational steering of experiments. The systems needed by stakeholders are geographically distributed, controlled by multiple independent, sometimes competing, organizations and are occasionally dynamically assembled for a short period of time. Currently, web-enabled simulations and grid computing infrastructures for physical simulations have concentrated on massively parallel applications and loose forms of code coupling wherein large-scale experiments are submitted as batch jobs. Computational steering of simulations based on analysis is usually not feasible due to the latencies involved. Several socio-technical systems of interest can be formally modeled as partially observed Markov decision processes (POMDP) and large n-way games; a key component of POMDP and such games is that actions taken by an observer change the system dynamics (e.g., isolating critical workers during an epidemic). In other words, the underlying complex network, individual behavior and dynamics of particular processes over the network (e.g., epidemic) co-evolve. Such mechanisms require computational steering, which in turn requires CI for coordinating resource discovery of computing and data assets; AI-based techniques for translating user level request to efficient workflows; re-using data sets whenever possible and spawning computer models with required initial parameters; and coordination of resources among various users. Computational steering also occurs at a coarser level as well --- in this case due to extremely large design space, we need to consider adaptive experimental designs. This is by and large infeasible on today’s grid computing environments.
